Time-scale transforms play a fundamental role in the compact representation of signals and images [1]. Non linear time representation provided a significant contribution to the definition of more flexible and adaptive transforms. However, in many applications signals are better characterized in the frequency domain. In particular, frequency distribution in the frequency axis is strictly dependent on the signal under study. On the contrary, frequency axis partition provided by conventional transforms obeys more rigid rules.

HVAC systems are the largest energy consumers in a building and a clean HVAC system can get about 11% in energy saving. Moreover, particulate pollution represents one of the main causes of cancer death and several health damages. This paper presents an innovative and not invasive procedure for the automatic indoor air quality assessment that depends on HVAC cleaning conditions. It is based on a mathematical algorithm that processes a few on-site physical measurements that are acquired by dedicated sensors at suitable locations with a specif-ic time table.

The phenomenologyand reported effects of meditation vary according to the technique practiced.While numerous studies have explored the cerebral mechanisms involved inmeditation, little research provides direct comparisons between the neuronalnetwork dynamics involved in different meditation techniques.

Motor learning induces changes in resting-state (RS) network properties in fronts-parietal (Albert et al, 2009) and sensorimotor (Taubert et al, 2011) networks. This study explores the putative modulations of spontaneous resting-state oscillations following a sensori-motor learning task. The task consisted in lifting a load with the right hand, which triggered the unloading of a load suspended to the left forearm (Paulignan et al., 1989). Because learning stabilizes quickly, a temporal delay was implemented, hence placing the subject in a dynamic learning state.

With the exponential increase in data dimension and methodological complexities, conducting brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. To date, most of the MEG/EEG processing is done by combining software packages and custom tools which often hinders reproducibility of the experimental findings. Here we describe NeuroPype, which is a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies.

This paper focuses on the use the Jensen Shannon divergence for guiding denoising. In particular, it aims at detecting those image regions where noise is masked; denoising is then inhibited where it is useless from the visual point of view. To this aim a reduced reference version of the Jensen Shannon divergence is introduced and it is used for determining a denoising map. The latter separates those image pixels that require to be denoised from those that have to be leaved unaltered.

The parallel implementation of MUPHY, a concurrent multiscale code for large-scale hemodynamic simulations in anatomically realistic geometries, for multi-GPU platforms is presented. Performance tests show excellent results, with a nearly linear parallel speed-up on up to 32GPUs and a more than tenfold GPU/CPU acceleration, all across the range of GPUs. The basic MUPHY scheme combines a hydrokinetic (Lattice Boltzmann) representation of the blood plasma, with a Particle Dynamics treatment of suspended biological bodies, such as red blood cells.

We give a complete characterization of bipartite graphs having tree-like Galois lattices. We prove that the poset obtained by deleting bottom and top elements from the Galois lattice of a bipartite graph is tree-like if and only if the graph is a bipartite distance hereditary graph. Relations with the class of Ptolemaic graphs are discussed and exploited to give an alternative proof of the result. (C) 2015 Elsevier B.V. All rights reserved.